Beekeepers' perceptions toward a new omics tool for monitoring bee health in Europe.

Pressures on honey bee health have substantially increased both colony mortality and beekeepers' costs for hive management across Europe. Although technological advances could offer cost-effective solutions to these challenges, there is little research into the incentives and barriers to technological adoption by beekeepers in Europe. Our study is the first to investigate beekeepers' willingness to adopt the Bee Health Card, a molecular diagnostic tool developed within the PoshBee EU project which can rapidly assess bee health by monitoring molecular changes in bees.

The proteomic content of Varroa destructor gut varies according to the developmental stage of its host.

The nutritional physiology of parasites is often overlooked although it is at the basis of host-parasite interactions. In the case of Varroa destructor, one of the major pests of the Western honey bee Apis mellifera, the nature of molecules and tissues ingested by the parasite is still not completely understood. Here, the V.

Composition and Quality of Honey Bee Feed: The Methodology and Monitoring of Candy Boards.

The nutritional status of a honey bee colony is recognized as a key factor in ensuring a healthy hive. A deficient flow of nectar and pollen in the honey bee colony immediately affects its development, making room for pathogen proliferation and, consequently, for a reduction in the activities and strength of the colony. It is, therefore, urgent for the beekeepers to use more food supplements and/or substitutes in apiary management, allowing them to address colony nutritional imbalances according to the beekeeper's desired results.

Large Orbital Moment and Dynamical Jahn-Teller Effect of AlCl-Phthalocyanine on Cu(100).

Submonolayer amounts of chloroaluminum-phthalocyanine on Cu(100) were studied with scanning tunneling spectroscopy. The molecule can be prepared in a fourfold symmetric state whose conductance spectrum exhibits a zero-bias feature similar to a Kondo resonance. In magnetic fields, however, this resonance splits far more than expected from the spin of a single electron.

Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble.

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist.